Multi-Objective Big Data Optimization with jMetal and Spark

Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data stored in the Hadoop File System (HDFS) in each evaluation step of a metaheuristic and to provide a software tool to solve these kinds of problems. This tool combines the jMetal multi-objective optimization framework with Apache Spark. We have carried out experiments to measure the performance of the proposed parallel infrastructure in an environment based on virtual machines in a local cluster comprising up to 100 cores. We obtained interesting results for computational e ort and propose guidelines to face multi-objective Big Data Optimization problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Performance enhancement of an immersed boundary method based FSI solver using OpenMP

This work presents a high-fidelity in-house Fluid Structure Interaction (FSI) solver devel- oped by combining discrete forcing Immersed Boundary Method (IBM) with a RK-4 based structural solver. Classification of the grid points as fluid, solid and IB points in the IBM framework and the solution of the pressure correction equations are the two most computationally expensive section in the numerical solver. These computational efforts can be significantly reduced by implementing OpenMP techniques. However, the successive over-relaxation (SOR) iterative method used in the serial code is not suitable for OpenMP parallelization as it shows data dependencies from previous iterations. Therefore, the Red-Black (RB) SOR is implemented to avoid the data dependencies

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance

Numerics of High Performance Computers and Benchmark Evaluation of Distributed Memory Computers

The internal representation of numerical data, their speed of manipulation to generate the desired result through efficient utilisation of central processing unit, memory, and communication links are essential steps of all high performance scientific computations. Machine parameters, in particular, reveal accuracy and error bounds of computation, required for performance tuning of codes. This paper reports diagnosis of machine parameters, measurement of computing power of several workstations, serial and parallel computers, and a component-wise test procedure for distributed memory computers. Hierarchical memory structure is illustrated by block copying and unrolling techniques. Locality of reference for cache reuse of data is amply demonstrated by fast Fourier transform codes. Cache and register-blocking technique results in their optimum utilisation with consequent gain in throughput during vector-matrix operations. Implementation of these memory management techniques reduces cache inefficiency loss, which is known to be proportional to the number of processors. Of the two Linux clusters-ANUP16, HPC22 and HPC64, it has been found from the measurement of intrinsic parameters and from application benchmark of multi-block Euler code test run that ANUP16 is suitable for problems that exhibit fine-grained parallelism. The delivered performance of ANUP16 is of immense utility for developing high-end PC clusters like HPC64 and customised parallel computers with added advantage of speed and high degree of parallelism

Performance Analysis Of Pde Based Parallel Algorithms On Different Computer Architectures

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, 2009Thesis (M.Sc.) -- İstanbul Technical University, Institute of Informatics, 2009Son yıllarda dağıtık algoritmaların farklı platformlarda kullanılabilmesi platform ve uygulama bağımsız performans analizi uygulamaları ihtiyacını arttırmıştır. Farklı donanımları ve haberleşme metodlarını destekleyen uygulamalar kullanıcılara donanım ve yazılımdan bağımsız ortak bir zemin hazırladıkları için kolaylık sağlamaktadır. Kısmi fark denklemleri hesaplamalı bilim ve mühendisliğin bir çok alanında kullanılmaktadır (ısı, dalga yayılımı gibi). Bu denklemlerin sayısal çözümü yinelemeli yöntemler kullanılarak yapılmaktadır. Problemin boyutu ve hata değerine göre çözüme ulaşmak için gereken yineleme sayısı ve buna bağlı olarak süresi değişmektedir. Kısmi fark denklemelerinin tek işlemcili bilgisayarlardaki çözümü uzun sürdüğü ve yüksek boyutlarda hafızaları yetersiz kaldığı için paralelleştirilerek birden fazla bilgisayarın işlemcisi ve hafızası kullanılarak çözülmektedir. Tezimde eliptik kısmi fark denklemlerini Gauss-Seidel ve Successive Over-Relaxation (SOR) metodlarını kullanarak çözen paralel algoritmalar kullanılmıştır. Performans analizi ve eniyilemesi kabaca üç adımdan oluşmaktadır; ölçüm, sonuçların analizi, darboğazların tespit edilip yazılımda iyileştirme yapılması. Ölçüm aşamasında programın koşarken ürettiği performans bilgisi toplanır, toplanan bu veriler görselleştirme araçları ile anlaşılır hale getirilerek yorumlanır. Yorumlama aşamasında tespit edilen dar boğazlar belirlenir ve giderilme yöntemleri araştırılır. Gerekli iyileştirmeler yapılarak program yeniden analiz edilir. Bu aşamaların her birinde farklı uygulamalar kullanılabilir fakat tez çalışmamda uygulamaları tek çatı altında toplayan TAU kullanılmıştır. TAU (Tuning and Analysis Utilities) farklı donanımları ve işletim sistemlerini destekleyerek farklı paralelleştirme metodlarını analiz edebilmektedir. Açık kaynak kodlu olan TAU diğer açık kaynak kodlu uygulamalar ile uyumlu olup birçok seviyede bütünleşme sağlanmıştır. Bu tez çalışmasında, iki farklı platformda aynı uygulamanın performans analizi yapılarak platform farkının getirdiği farklılıklar incelenmektedir. Performans analizinde bir algoritmanın eniyilemesini yapmak için genel bir kural olmadığından her algoritma her platformda incelenerek gerekli değişiklikler yapılmalıdır. Bu bağlamda kullandığım PDE algoritmasının her iki sistemdeki analizi sonucu elde edilen bilgiler yorumlanmıştır.In last two decades, use of parallel algorithms on different architectures increased the need of architecture and application independent performance analysis tools. Tools that support different communication methods and hardware prepare a common ground regardless of equipments provided. Partial differential equations (PDE) are used in several applications (such as propagation of heat, wave) in computational science and engineering. These equations can be solved using iterative numerical methods. Problem size and error tolerance effects iteration count and computation time to solve equation. PDE computations take long time using single processor computers with sequential algorithms, and if data size gets bigger single processors memory may be insufficient. Thus, PDE?s are solved using parallel algorithms on multiple processors. In this thesis, elliptic partial differential equation is solved using Gauss-Seidel and Successive Over-Relaxation (SOR) methods parallel algorithms. Performance analysis and optimization basically has three steps; evaluation, analysis of gathered information, defining and optimizing bottlenecks. In evaluation, performance information is gathered while program runs, then observations are made on gathered information by using visualization tools. Bottlenecks are defined and optimization techniques are researched. Necessary improvements are made to analyze the program again. Different applications in each of these stages can be used but in this thesis TAU is used, which collects these applications under one roof. TAU (Tuning and Analysis Utilities) supports many hardware, operating systems and parallelization methods. TAU is an open source application and collaborates with other open source applications at different levels. In this thesis, differences based on performance analysis of an algorithm in different two architectures are investigated. In performance analysis and optimization there is no golden rule to speed up algorithm. Each algorithm must be analyzed on that specific architecture. In this context, the performance analysis of a PDE algorithm on two architectures has been interpreted.Yüksek LisansM.Sc